Communication–computation tradeoff in distributed consensus optimization for MPC-based coordinated control under wireless communications

This paper presents an analysis of the tradeoff between repeated communications and computations for a fast distributed computation of global decision variables in a model-predictive-control (MPC)-based coordinated control scheme. We consider a coordinated predictive control problem involving uncertain and constrained subsystem dynamics and employ a formulation that presents it as a distributed optimization problem with sets of local and global decision variables where the global variables are allowed to be optimized over a longer time interval. Considering a modified form of the dual-averaging-based distributed optimization scheme, we explore convergence bounds under ideal and non-ideal wireless communications and determine the optimal choice of communication cycles between computation steps in order to speed up the convergence per unit time of the algorithm. We apply the algorithm for a class of dynamic-policy based stochastic coordinated control problems and illustrate the results with a simulation example.